The article titled “Network Delay Forecast and Master–Slave Consistency Enhancement for Remote Surgical Robots” explores the critical impact of network delays on remote surgical procedures. Authored by Jinhua Li, Chi Zhang, Bo Guan, Haitao Niu, and Jianchang Zhao, the study emphasizes the necessity of maintaining master-slave motion consistency to ensure surgical safety during remote operations.

Background and Importance

With the rise of remote surgical robots, the need for reliable and efficient communication systems has become paramount. Remote surgeries, particularly those conducted over long distances using technologies like 5G, are susceptible to network delays that can adversely affect the performance of surgical robots. Delays exceeding 200 milliseconds can be considered unsafe, yet there is no universally accepted latency threshold. The research highlights the importance of real-time network delay acquisition and forecasting to enhance the reliability of remote surgical systems.

Methodology

The researchers propose a multi-faceted approach to address the challenges posed by network delays. Initially, they perform real-time calibration of unidirectional network delays. This is followed by employing a real-time training parallel recurrent neural network to forecast network delays, which enables timely safety warnings. Additionally, they enhance the master-slave motion consistency by forecasting the position of the slave manipulator in real-time.

The study employs a comprehensive dataset acquired from a remote communication platform, which includes various parameters essential for the operation of the robotic system. The methodology also includes rigorous testing of different forecasting methods to evaluate their effectiveness in maintaining motion consistency.

Results

The findings reveal that the proposed forecasting method significantly reduces the impact of network delays on master-slave motion consistency, achieving a reduction to approximately 20%–80% of the original delay levels. The program demonstrates strong generalization capabilities and is capable of operating over distances of at least 630 kilometres.

Conclusions and Future Directions

The research concludes that real-time delay forecasting can substantially mitigate the adverse effects of network latency in remote surgeries, thus enhancing the safety and consistency of master-slave robotic systems. However, the authors acknowledge limitations in the accuracy of forecasts under high-frequency motions and the need for further comparisons with other forecasting methods.

Future work is suggested to include optimising data sampling frequencies, implementing additional input parameters for delay forecasts, and improving the mechanisms for recognizing and forecasting master manipulator motion.

This study provides valuable insights into the integration of advanced forecasting techniques in surgical robotics, promising enhanced safety and efficiency in remote surgical practices.

READ MORE… https://onlinelibrary.wiley.com/doi/full/10.1002/rcs.70048

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